A Story to Explain Genetic Algorithms
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A Story to Explain Genetic Algorithms
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Re: A Story to Explain Genetic Algorithms
#2Neat little story, despite the grammar and capitalization. Is it common to use _ as an index variable?
Edit: for _ in range(4):
Re: A Story to Explain Genetic Algorithms
#3Neat little story, despite the grammar and capitalization. Is it common to use _ as an index variable? Edit: for _ in range(4):
Not sure how common that is, but I've seen it used before where the index variable itself is not referenced inside the body of the statement.
Re: A Story to Explain Genetic Algorithms
#4Neat little story, despite the grammar and capitalization. Is it common to use _ as an index variable? Edit: for _ in range(4):
_ is a typical name to use when the language requires a variable name that the program doesn't otherwise need. In this case, the program just needs to run some code four times, and doesn't care about the index. The language doesn't have a plain "run this code X times" construct, but it does have a "iterate over this list" construct, so the code iterates over the list [0, 1, 2, 3] and puts the current element into a dummy variable since it's not being used.
Re: A Story to Explain Genetic Algorithms
#5Neat little story, despite the grammar and capitalization. Is it common to use _ as an index variable? Edit: for _ in range(4):
I've seen it before, but it's a little jarring. Not sure what's wrong with just using i.
Re: A Story to Explain Genetic Algorithms
#6If you're interested in the world of "computational intelligence" aka "nature-inspired computation", this book is a good high level survey: http://www.cleveralgorithms.com/
(You can also find stuff under the heading of "metaheuristics" -- http://cs.gmu.edu/~sean/book/metaheuristics/).
GAs are one of four (!) different independently developed strands of thought -- Genetic Algorithms, Genetic Programming, Evolutionary Programming and Evolutionary Strategies. Look out for that when you hit the Googletrons.
Re: A Story to Explain Genetic Algorithms
#7> Then our Charles simply had to figure out how Mrs Kipling scored the cakes and he could genetically evolve the best cake!
"And then a miracle happens." Isn't the fitness function the "key ingredient?" (It's in the source code, but not in the text for a reason.)
Still a good explanation of the rest.
Re: A Story to Explain Genetic Algorithms
#8If you're interested in the world of "computational intelligence" aka "nature-inspired computation", this book is a good high level survey: http://www.cleveralgorithms.com/ (You can also find stuff under the heading of "metaheuristics" -- http://cs.gmu.edu/~sean/book/metaheuristics/ ). GAs are one of four (!) different independently developed strands of thought -- Genetic Algorithms, Genetic Programming, Evolutionary…
Those are some good links, thanks. Are there clear distinctions between these four areas? It seems like the separation is a historical artifact, when they're all basically doing the same thing.
Re: A Story to Explain Genetic Algorithms
#9If you're interested in the world of "computational intelligence" aka "nature-inspired computation", this book is a good high level survey: http://www.cleveralgorithms.com/ (You can also find stuff under the heading of "metaheuristics" -- http://cs.gmu.edu/~sean/book/metaheuristics/ ). GAs are one of four (!) different independently developed strands of thought -- Genetic Algorithms, Genetic Programming, Evolutionary…
Those are some good links, thanks. Are there clear distinctions between these four areas? It seems like the separation is a historical artifact, when they're all basically doing the same thing.
I mostly looked at GAs and GPs. Evo strategy has some interesting mathematical stuff.
Basically GA and GP are the dominant strands. And there's a key difference.
Genetic Algorithms are historically about finding paramaters into a function. You have some function f(X1 ... Xn), and you are trying to find the best set of parameters X1...Xn. So a common representation is a string of floats or integers or whatever, and then you do the crossovers and mutations based on positions in the string.
Genetic Programming is about creating new functions. So instead of a string of parameters, the genome is (usually) a tree of instructions to be interpreted. You do mutations by swapping node types, crossovers by chopping subtrees and moving them etc.
One artefact of the historical "parallel evolution" (teehee) of these fields is that GP practitioners use crossovers much less than GA practitioners.
Re: A Story to Explain Genetic Algorithms
#10Neat little story, despite the grammar and capitalization. Is it common to use _ as an index variable? Edit: for _ in range(4):
_ is a typical name to use when the language requires a variable name that the program doesn't otherwise need. In this case, the program just needs to run some code four times, and doesn't care about the index. The language doesn't have a plain "run this code X times" construct, but it does have a "iterate over this list" construct, so the code iterates over the list [0, 1, 2, 3] and puts the current element into a d…
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